Preventing Fraud Through Customer Onboarding Optimization: 10 Key Insights

A recent webinar featuring experts Zoya Khurana, Lead Product Manager at Alloy, Brady Harrison, Head of Strategy and Execution, Digital Solutions at Equifax, and Chris Hansen, General Manager, Strategic Partnerships at Nova Credit, addressed the increasing sophistication of digital fraud and its impact on customer onboarding and credit risk evaluation. The panel discussed how only relying on legacy methods negatively affects both consumers and lenders, highlighting the need for process transformation to prevent fraud and improve customer experience.

Here are the top 10 key insights lenders should transform their business in 2025 and beyond: 

1. Gen AI is a Game Changer (and a Fraud Enabler): The rise of Generative AI is rapidly changing the fraud landscape. Fraudsters can now outsmart traditional authentication methods. Businesses must adapt to this new reality by embracing AI and ML for enhanced detection and authentication.

2. Authenticate the Person, Not Just the Document: The focus needs to shift from mere document verification to authenticating the person behind it. Orchestrating alternative data, event data, AI predictions of synthetic identities, and direct consumer verification of income can create a clearer picture of the applicant. Confirming the real existence of the applicant is paramount in the era of Gen AI-generated forgeries.

3. Adopt Integrated Fraud Checks Throughout the Customer Journey: Move away from isolated fraud checks. Implement integrated workflows that combine identity, income, and behavioral signals. This comprehensive approach provides a more holistic view and catches fraudulent activity more effectively than siloed checks.

4. Consider the Entire Consumer Journey: Fraud can occur at any point in the consumer journey. It's crucial to understand the entire flow from application through funding. Implement redundancy by design across fraud controls at each stage to prevent vulnerabilities.

5. Orchestrate Data Sources Thoughtfully: Partnering with data providers and strategically using various data sources is key. Identify potential fraud indicators early and leverage the right data at the right time. Thoughtful data orchestration can significantly enhance fraud detection.

6. Document Fraud is Rampant: Document fraud, including pay stubs with manipulated metadata, is widespread. Businesses need robust document solutions that not only verify authenticity but also flag potential fraud signals.  Relying solely on visual document verification is no longer sufficient. Be aware that a significant percentage of submitted documents might be fraudulent.

7. Differentiate Between Fraud Types: Understanding the spectrum of fraud is vital. Distinguish between first-party (misrepresentation), third-party (identity theft), and synthetic (fabricated identity) fraud. This understanding helps in tailoring specific detection and prevention strategies for each type.

8. Alternative Data is Essential for Real-Time Detection: Real-time alternative data sources are invaluable in detecting fraud. Authentic behavior and volatility over time are difficult to fake. Continuous analysis of detection patterns provides real-time insights and helps identify synthetic identities more efficiently.

9. Synthetic Fraud Poses a Major Threat: Synthetic identity fraud is a significant concern - for both consumer and business, accounting for a substantial portion of lending portfolio write-offs. It bypasses traditional credit profiling, making it hard to detect. Cash flow data and continuous analysis are crucial in combating this sophisticated form of fraud.

10. Preserve Optionality and Adaptability: The fraud landscape is constantly evolving, often faster than tech roadmaps. Businesses must build adaptable systems that allow them to quickly integrate new tools and data sources. Preserving optionality ensures you can respond to emerging fraud vectors and payment trends without overhauling your entire infrastructure.